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Hajdu András

Hajdu András

Hajdu András
DE > IK
Komputergrafika és Képfeldolgozás Tanszék
egyetemi tanár, tanszékvezető 2008-
Név: Hajdu András
További profilok: Google Scholar, MTMT
Fokozat
  • PhD, Debreceni Egyetem (2003)
  • Habilitáció, Debreceni Egyetem (2008)
  • MTA doktora, MTA (2017)
Szakterület: matematikus, informatikus
Ajánlott linkek:
Önéletrajz: letöltés

Teljes publikációs lista

A lista áttöltése az MTMT rendszerébe
Hiányzó közlemények feltöltése
Hitelesített Publikációs Lista igénylése
OA letöltési statisztika megtekintése
Feltöltött közlemény:
201
DEA-ban:
192
OA:
25
Publikációs időszak:
1997-2022
2022
  1. Kapusi, T., Erdei, T., Husi, G., Hajdu, A.: Application of Deep Learning in the Deployment of an Industrial SCARA Machine for Real-Time Object Detection.
    Robotics. 11 (4), 1-20, 2022.
    Folyóirat-mutatók:
    Q2 Artificial Intelligence (2021)
    Q2 Control and Optimization (2021)
    Q1 Mechanical Engineering (2021)
  2. Hajdu, A., Terdik, G., Tiba, A., Tomán, H.: A stochastic approach to handle resource constraints as knapsack problems in ensemble pruning.
    Mach. Learn. 111 1551-1595, 2022.
    Folyóirat-mutatók:
    Q1 Artificial Intelligence (2021)
    Q1 Software (2021)
  3. Huang, X., Zhou, S., Tóth, J., Hajdu, A.: Cuproptosis-related gene index: a predictor for pancreatic cancer prognosis, immunotherapy efficacy, and chemosensitivity.
    Front. Immunol. Epub 1-31, 2022.
    Folyóirat-mutatók:
    Q1 Immunology (2021)
    Q1 Immunology and Allergy (2021)
  4. Kapusi, T., Kovács, L., Hajdu, A.: Deep learning-based anomaly detection for imaging in autonomous vehicles.
    In: 2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS): Proceedings (2022.05.16-18.)(Debrecen). Szerk.: Fazekas István, IEEE, Piscataway (NJ), 142-147, 2022. ISBN: 9781665496520
  5. Bogacsovics, G., Tóth, J., Hajdu, A., Harangi, B.: Enhancing CNNs through the use of hand-crafted features in automated fundus image classification.
    Biomed. Signal Process. Control. 76 1-10, 2022.
    Folyóirat-mutatók:
    Q1 Health Informatics (2021)
    Q1 Signal Processing (2021)
  6. Pándy, Á., Kun, D., Kovács, L., Vasváry, G., Pánti, Z., Hajdu, A.: Image sensor based steering signal for a digital actuator system.
    In: 2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) / Fazekas István, IEEE, Piscataway, 229-234, 2022. ISBN: 9781665496537
2021
  1. Bogacsovics, G., Hajdu, A., Harangi, B., Lakatos, I., Lakatos, R., Szabó, M., Tiba, A., Tóth, J., Tarcsi, Á.: Adatelemzési folyamat és keretrendszer a közigazgatás számára.
    Közigazgatástudomány. 1 (2), 146-158, 2021.
  2. Bogacsovics, G., Hajdu, A., Harangi, B.: Cell Segmentation in Digitized Pap Smear Images Using an Ensemble of Fully Convolutional Networks.
    In: 2021 IEEE Signal Processing in Medicine and Biology Symposium : Proceedings, IEEE, Philadelphia, 1-6, 2021. ISBN: 9781665428972
  3. Lantang, O., Terdik, G., Hajdu, A., Tiba, A.: Comparison of single and ensemble-based convolutional neural networks for cancerous image classification.
    Ann. Math. Inform. 54 45-56, 2021.
    Folyóirat-mutatók:
    Q3 Computer Science (miscellaneous)
    Q4 Mathematics (miscellaneous)
  4. Ahammed, A., Harangi, B., Hajdu, A.: Hybrid AdaBoost and Naïve Bayes classifier for supervised learning.
    In: Proceedings of the 1st Conference on Information Technology and Data Science. Ed.: István Fazekas, András Hajdu, Tibor Tómács, CEUR Workshop Proceedings, Debrecen, 1-18, 2021, (CEUR Workshop Proceedings, ISSN 1613-0073 ; 2874.)
  5. Lantang, O., Terdik, G., Hajdu, A., Tiba, A.: Investigation of the efficiency of an interconnected convolutional neural network by classifying medical images.
    Ann. Math. Inform. 53 219-234, 2021.
    Folyóirat-mutatók:
    Q3 Computer Science (miscellaneous)
    Q4 Mathematics (miscellaneous)
  6. Lakatos, I., Hajdu, A., Harangi, B.: Molecule Classification Using Visualization and Convolutional Neural Network.
    In: IEEE 18th International Symposium on Biomedical Imaging (ISBI), IEEE, Piscataway, 1695-1698, 2021.
  7. Bogacsovics, G., Hajdu, A., Lakatos, R., Beregi-Kovács, M., Tiba, A., Tomán, H.: Replacing the SIR epidemic model with a neural network and training it further to increase prediction accuracy.
    Ann. Math. Inform. 53 73-91, 2021.
    Folyóirat-mutatók:
    Q3 Computer Science (miscellaneous)
    Q4 Mathematics (miscellaneous)
2020
  1. Harangi, B., Baran, Á., Hajdu, A.: Assisted deep learning framework for multi-class skin lesion classification considering a binary classification support.
    Biomed. Signal Process. Control. 62 1-7, 2020.
    Folyóirat-mutatók:
    Q2 Health Informatics
    Q2 Signal Processing
  2. Beregi-Kovács, M., Baran, Á., Hajdu, A.: Efficient Learning of Model Weights via Changing Features During Training.
    2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES) 2020 43-48, 2020.
  3. Tóth, J., Tomán, H., Hajdu, A.: Efficient sampling-based energy function evaluation for ensemble optimization using simulated annealing.
    Pattern Recognit. 107 1-12, 2020.
    Folyóirat-mutatók:
    D1 Artificial Intelligence
    D1 Computer Vision and Pattern Recognition
    D1 Signal Processing
    D1 Software
  4. Porwal, P., Pachade, S., Kokare, M., Deshmukh, G., Son, J., Bae, W., Liu, L., Wang, J., Liu, X., Gao, L., Wu, T., Xiao, J., Wang, F., Yin, B., Wang, Y., Danala, G., He, L., Choi, Y., Lee, Y., Jung, S., Li, Z., Sui, X., Wu, J., Li, X., Zhou, T., Tóth, J., Baran, Á., Kori, A., Chennamsetty, S., Safwan, M., Alex, V., Lyu, X., Cheng, L., Chu, Q., Li, P., Ji, X., Zhang, S., Shen, Y., Dai, L., Saha, O., Sathish, R., Melo, T., Araújo, T., Harangi, B., Sheng, B., Fang, R., Sheet, D., Hajdu, A., Zheng, Y., Mendonça, A., Zhang, S., Campilho, A., Zheng, B., Shen, D., Giancardo, L., Quellec, G., Mériaudeau, F.: IDRiD: Diabetic Retinopathy: segmentation and grading challenge.
    Med. Image Anal. 59 1-26, 2020.
    Folyóirat-mutatók:
    D1 Computer Graphics and Computer-Aided Design
    D1 Computer Vision and Pattern Recognition
    D1 Health Informatics
    D1 Radiological and Ultrasound Technology
    D1 Radiology, Nuclear Medicine and Imaging
2019
  1. Tóth, J., Kapusi, T., Harangi, B., Tomán, H., Hajdu, A.: Accelerating the Optimization of a Segmentation Ensemble using Image Pyramids.
    In: 11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019). Eds.: S. Lončarić, R. Bregović, M. Carli, M. Subašić, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, USA, 43-48, 2019. ISBN: 9781728131405
  2. Harangi, B., Tóth, J., Baran, Á., Hajdu, A.: Automatic screening of fundus images using a combination of convolutional neural network and hand-crafted features.
    In: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Ed.: Riccardo Barbieri, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, USA, 2699-2702, 2019. ISBN: 9781538613122
  3. Harangi, B., Tóth, J., Bogacsovics, G., Kupás, D., Kovács, L., Hajdu, A.: Cell detection on digitized Pap smear images using ensemble of conventional image processing and deep learning techniques.
    In: 11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019). Eds.: S. Lončarić, R. Bregović, M. Carli, M. Subašić, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, USA, 38-42, 2019. ISBN: 9781728131405
  4. Lantang, O., Tiba, A., Hajdu, A., Terdik, G.: Convolutional Neural Network For Predicting The Spread of Cancer.
    In: Proceedings of the 10th IEEE International Conference on Cognitive Infocommunications : CogInfoCom 2019. Szerk.: Péter Baranyi, IEEE-Inst Electrical Electronics Engineers Inc, Piscataway, 175-180, 2019. ISBN: 9781728147932
Mindet mutasd
frissítve: 2023-01-29, 01:12

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Tudományos folyóiratcikkek száma: 68
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